DeepASLR: A CNN based human computer interface for American Sign Language recognition for hearing-impaired individuals

KASAPBAŞI, Ahmed; ELBUSHRA, Ahmed Eltaye AHMED; AL-HARDANEE, Omar; Yılmaz, Arif

doi:10.1016/j.cmpbup.2021.100048

Cited by 47 publications

(19 citation statements)

References 6 publications

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“…The result shows that the system has a recognition rate of 97%. In [ 36 ], the authors created a dataset and a CNN sign language recognition system to interpret the American sign gesture alphabet and translate it to our natural language. Three datasets were used to compare the results and accuracy of each.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Technology to Recognize American Sign Language Alphabet

Alsharif,

Altaher,

Altaher

et al. 2023

Sensors

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Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet. The primary objective of this study was to leverage contemporary technology to bridge the communication gap between hearing-impaired individuals and individuals with no hearing impairment. The models utilized in this research include AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer were trained and tested using an extensive dataset comprising over 87,000 images of the ASL alphabet hand gestures. Numerous experiments were conducted, involving modifications to the architectural design parameters of the models to obtain maximum recognition accuracy. The experimental results of our study revealed that ResNet-50 achieved an exceptional accuracy rate of 99.98%, the highest among all models. EfficientNet attained an accuracy rate of 99.95%, ConvNeXt achieved 99.51% accuracy, AlexNet attained 99.50% accuracy, while VisionTransformer yielded the lowest accuracy of 88.59%.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Technology to Recognize American Sign Language Alphabet

Alsharif,

Altaher,

Altaher

et al. 2023

Sensors

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show abstract

“…The proposed system was developed to improve the information communication among people with hearing loss and speech impairment. In a recent study [29] a new dataset, called the American sign language alphabet (ASLA), was created by considering various conditions, such as lighting and distance, and letter finger images were classified using a CNN. The authors demonstrated by using the ASLA dataset, their system can achieve a higher performance than previous studies that used different datasets.…”

Section: A Study Using Deep Learningmentioning

confidence: 99%

ASL Recognition by the Layered Learning Model Using Clustered Groups

Shin¹,

Jung²

2023

Computer Systems Science and Engineering

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American Sign Language (ASL) images can be used as a communication tool by determining numbers and letters using the shape of the fingers. Particularly, ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons, because sign language is their only channel of expression. Representative ASL recognition methods primarily adopt images, sensors, and pose-based recognition techniques, and employ various gestures together with hand-shapes. This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers. In the proposed model, the collected ASL images were clustered based on similarities in shape, and clustered group classification was first performed, followed by reclassification within the group. The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition. After selecting the optimized group, we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing. The proposed model exhibited an improved performance compared with the general classification model.

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“…Computer Vision is a field of Artificial Intelligence that focuses on problems related to images and videos. CNN [1][2][3][5] combined with Computer vision is capable of performing complex problems. To develop a practical and meaningful system that can able to understand sign language and translate that to the corresponding text.…”

Section: ) Sign Language Recognitionmentioning

confidence: 99%

“…From the above literature survey, the author uses different techniques to implement and develop the model which is based on a visionbased approach, sensors, MOPGRU ( Mediapipe optimized gated recurrent unit) [4], CNN(Convolutional Neural Networks) [1][2][3] and [5], which is used for image recognition and tasks that involve the processing of pixel data. LSTM is used to learn, process, and classify sequential data because these networks can learn long-term dependencies between time steps of data.…”

Section: ) Sign Language Recognition Via Skeleton-aware Multi-model E...mentioning

confidence: 99%

A Survey Paper on Sign Language Recognition System using OpenCV and Convolutional Neural Network

Tambuskar¹,

Khopde²,

Ghode³

et al. 2023

IJRASET

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Communication is a very important part of our Human life to express feelings and thoughts. People like the Deaf & Dumb always face difficulty as they cannot speak in their regional languages. Language performs a very important role in communication, it can be verbal i.e. using words to speak, read and write or non-verbal using facial expressions and sign language. So, people like the Deaf and Dumb have the only choice to speak in Sign language means non-verbal. However, Sign language is a very important mode of their community. But it is difficult for people who are unaware of sign language. Hence, here is a system “Sign Language Recognition System Using Open CV and Convolutional Neural Network”. We proposed a system that converts sign language to their appropriate alphabet, words in a standard language to make easily understood by all. We also make some default gestures that we daily use in our day-to-day life. The project works on a learning algorithm, it requires the collection of datasets which includes images of each alphabet, and digits to train the model. For the classification of the image convolutional neural network is used. Also for accessing the camera and taking input we used an open cv.

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DeepASLR: A CNN based human computer interface for American Sign Language recognition for hearing-impaired individuals

Cited by 47 publications

References 6 publications

Deep Learning Technology to Recognize American Sign Language Alphabet

Deep Learning Technology to Recognize American Sign Language Alphabet

ASL Recognition by the Layered Learning Model Using Clustered Groups

A Survey Paper on Sign Language Recognition System using OpenCV and Convolutional Neural Network

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